Project Summary The overall objective of this research project is to develop a novel approach for high throughput screening of individual cells based on holographic imaging. To achieve this goal, we propose to implement a new quantitative phase imaging modality, holographic cytomtery, which incorporates several novel technical advances to enable high throughput imaging. Holographic cytometry (HC) will bring the high sensitivity of quantitative phase microscopy (QPM) to imaging of cells flowing through microfluidic devices. While QPM has been used for cell analysis previously, typically only a handful of cells have been imaged. To enable significant application of QPM for fundamental cell biology and clinical studies, it is necessary to move to a high throughput implementation. Technical advances needed to realize the high resolution HC system include use of high speed line scan cameras, microfluidic chips with multiple parallel channels, and light from a pulsed laser source to enable stroboscopic illumination. In order to efficiently analyze and process this data set, rapid analysis software will be developed that leverages the highly parallel processing capabilities of graphics processing units and machine learning algorithms to enable automated classification. The proposed HC method can be applied to imaging a wide range of flowing cells. To demonstrate the utility of the approach, we will initially target the measurement of cancerous progression due to environmental toxicant exposure. We have conducted a preliminary study that shows QPM can detect early changes in the biomechanical properties of cells due to arsenic exposure. In the proposed project, we seek to develop QPM based biomarkers of pre-cancerous change that will enable rapid assessesment. QPM has not been implemented in such a format to date and thus is not yet a feasible approach for clinical or research studies. To meet the goal of high throughput imaging with QPM, the following Specific Aims are proposed: 1. Develop new instrumentation for high speed imaging using off axis digital holography. 2. Implement high throughput analysis methods based on machine learning 3. Test and validate high throughput system with pilot studies of heavy metal exposed epithelial cells to show the approach can detect early pre-cancerous changes due to environmental toxicant exposure. Upon completion of this project, we will have realized a high throughput imaging cytometry system for research and clinical applications.